Channel estimation and signal detection in NOMA is a major challenge due to error propagation in SIC for NOMA networks.
FSL algorithms can be used for
channel estimation and multi-user detection in downlink NOMA networks, where each user performs a supervised learning (SL) algorithm for channel estimation and signal detection of multiple users due to SIC and send
their local federated learning model parameters to the BS that will generate the global
FL model.
As in [1]}, FSL can detect multi-user signal in multi-cell uplink NOMA networks via iteratively transmitting individually learning model parameters from all BSs to a server and broadcasting the unified learning model parameters from the server to all BSs.
Further, FSL can be used to
automatically design the codebook of BSs and decoding strategy of users for code-domain NOMA networks so as to minimize bit error rate [2]}, where users upload the learned result to the corresponding BSs and the BSs forward their unified learned result to a server.
